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bioRxiv · 10.1101/2025.07.03.663046

Parameter Optimization of Biochemical Models for Precision Medicine: A Case Study in PI(4,5)P2 Synthesis

Hernandez-Hernandez, G.·Tieu, M.·Yang, P.-C.·Santana, L. F.·Clancy, C. E.
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Abstract

Precision medicine requires models that can translate rich molecular measurements into individualized predictions of biological response. This challenge is particularly acute for phosphoinositide signaling disorders that often exhibit cell-type-specific responses to identical genetic or pharmacological perturbations. Here, we develop a New Approach Methodology (NAM) demonstrating that basal phosphoinositide pool composition, determined by the size of the PI(4)P reserve, determines the robustness of lipid signaling. The NAM comprises a core kinetic model of phosphatidylinositol (PI), phosphatidylinositol 4-phosphate (PI(4)P), phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2), and inositol 1,4,5-trisphosphate (IP3) dynamics. The model also incorporates phospholipase C (PLC)-mediated hydrolysis and phosphatase-mediated turnover and explicitly accounts for IP3 biosensor binding during parameter optimization. Parameters were optimized using experimental measurements from superior cervical ganglion (SCG) neurons and validated against independent dose-dependent PI(4,5)P2 depletion data. Local and global sensitivity analyses were performed to identify the dominant parameter drivers of pathway behavior. These sensitivity relationships were then used to generate a population of model variants that captured phosphoinositide dynamics observed in tsA201 cells, human neuroblastoma cells, and hippocampal neurons. To infer cell-specific models, we developed two complementary inverse methods: sensitivity fingerprinting derived from mechanistic model sensitivities and a neural network trained on synthetic phosphoinositide time series. Both approaches reproduced experimental PI(4)P, PI(4,5)P2, and IP3 dynamics across cell types while preserving the baseline model structure. Importantly, the inferred models predicted experimentally observed differential vulnerability to kinase perturbation without additional fitting. Hippocampal neurons with large basal pools of PI(4)P maintained PI(4,5)P2 and IP3 signaling under phosphatidylinositol 4-kinase alpha (PI4KA) inhibition, whereas cells with small basal PI(4)P pools exhibited signaling failure. Simulations of PI4KA and phosphatidylinositol-4-phosphate 5-kinase type 1 gamma (PIP5K1C) loss-of-function mutations under repeated stimulation further revealed progressive signaling collapse in small-pool neurons but sustained function in large-pool neurons, demonstrating that basal lipid composition can determine genetic vulnerability. Together, this NAM provides a predictive, cell-specific framework for translating dynamic lipid measurements into mechanistic models that support precision medicine applications in phosphoinositide-related disorders.

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Hernandez-Hernandez, G., Tieu, M., Yang, P.-C., Santana, L. F., Clancy, C. E.. 2025-07-07. Parameter Optimization of Biochemical Models for Precision Medicine: A Case Study in PI(4,5)P2 Synthesis. https://doi.org/10.1101/2025.07.03.663046

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